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Development and Implementation of an Experimental Machine to Study Woven Fabric Preforming Defects

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Abstract

In Liquid Composite Molding (LCM), a fabric can be formed by highly double curved punch geometries. During the forming step, various types of defects can appear and may have a significant influence on the mechanical properties of the final composite materials. Till now, these defects have not been fully studied and/or understood. The aim of this study was to understand the mechanisms leading to the forming of mesoscopic defects (buckles and sliding), as well as their effect on the behavior of composite materials. To achieve this goal, an experimental machine was designed and built. The machine generates different types of defects, with controlled and adjusted amplitudes (calibrated defects) in samples of a fabric. These samples were then used to manufacture composite samples with calibrated defects, by an LCM process, in order to test and compare them with composite samples without defects. Thanks to this machine, calibrated forming defects that simulate the defects of an LCM process were generated, and the experimental parameters corresponding to the appearance of these defects were defined for two types of fabrics based on glass and carbon fibers. This work provides insight into the forming mechanisms of the buckle and sliding defects that occur during an LCM process.

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Acknowledgments

The authors would like to thank the Centre Val-de-Loire for financing this research.

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Authors and Affiliations

  1. LaMé (EA 7494), University of Orléans, University of Tours, INSA Centre Val de Loire, F45072, Orléans, France

    A. Shanwan, J. Gillibert & G. Hivet

  2. ITheMM (EA 7548), University of Reims Champagne-Ardenne, EiSINe, Campus Sup Ardenne, 9A rue Claude Chrétien, 08000, Charleville Mézières, France

    S. Allaoui

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  1. A. Shanwan
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Correspondence to S. Allaoui.

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Shanwan, A., Allaoui, S., Gillibert, J. et al. Development and Implementation of an Experimental Machine to Study Woven Fabric Preforming Defects. Exp Tech 46, 299–316 (2022). https://doi.org/10.1007/s40799-021-00483-z

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